




import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


snake_case__			:    str			     =      getLogger(__name__)


def     lowercase		(  _lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase = 8 ,  _lowerCAmelCase = 1024 ,  _lowerCAmelCase="val" ,  _lowerCAmelCase=None ,  _lowerCAmelCase=False ,  _lowerCAmelCase="summarization" ,  _lowerCAmelCase=None ,  _lowerCAmelCase=1 ,  _lowerCAmelCase = None ,  _lowerCAmelCase="" ,  **_lowerCAmelCase ,  ):
  UpperCAmelCase__         = str(UpperCamelCase__   )
  assert local_rank is not None
  torch.distributed.init_process_group(backend="""nccl""" ,  rank=UpperCamelCase__   )

  UpperCAmelCase__         = Path(UpperCamelCase__   )
  UpperCAmelCase__         = save_dir.joinpath(F'''rank_{local_rank}_output.json'''   )
  torch.cuda.set_device(UpperCamelCase__   )
  UpperCAmelCase__         = AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__   ).cuda()
  if fpaa:
    UpperCAmelCase__         = model.half()
  # determine if we need to increase num_beams
  use_task_specific_params(UpperCamelCase__ ,  UpperCamelCase__   )  # update config with task specific params
  UpperCAmelCase__         = generate_kwargs.pop("""num_beams""" ,  model.config.num_beams   )  # AttributeError risk?
  if num_return_sequences > num_beams:
    UpperCAmelCase__         = num_return_sequences

  UpperCAmelCase__         = AutoTokenizer.from_pretrained(UpperCamelCase__   )
  logger.info(F'''Inferred tokenizer type: {tokenizer.__class__}'''   )  # if this is wrong, check config.model_type.

  if max_source_length is None:
    UpperCAmelCase__         = tokenizer.model_max_length
  if prefix is None:
    UpperCAmelCase__         = prefix or getattr(model.config ,  """prefix""" ,  """"""   ) or """"""
  UpperCAmelCase__         = SeqaSeqDataset(
      UpperCamelCase__ ,  UpperCamelCase__ ,  UpperCamelCase__ ,  max_target_length=1024 ,  type_path=UpperCamelCase__ ,  n_obs=UpperCamelCase__ ,  prefix=UpperCamelCase__ ,  **UpperCamelCase__ ,  )
  # I set shuffle=True for a more accurate progress bar.
  # If all the longest samples are first, the prog bar estimate is too high at the beginning.
  UpperCAmelCase__         = ds.make_sortish_sampler(UpperCamelCase__ ,  distributed=UpperCamelCase__ ,  add_extra_examples=UpperCamelCase__ ,  shuffle=UpperCamelCase__   )
  UpperCAmelCase__         = DataLoader(UpperCamelCase__ ,  sampler=UpperCamelCase__ ,  batch_size=UpperCamelCase__ ,  collate_fn=ds.collate_fn   )
  UpperCAmelCase__         = []
  for batch in tqdm(UpperCamelCase__   ):
    UpperCAmelCase__         = model.generate(
        input_ids=batch["""input_ids"""].to(model.device   ) ,  attention_mask=batch["""attention_mask"""].to(model.device   ) ,  num_return_sequences=UpperCamelCase__ ,  num_beams=UpperCamelCase__ ,  **UpperCamelCase__ ,  )
    UpperCAmelCase__         = tokenizer.batch_decode(UpperCamelCase__ ,  skip_special_tokens=UpperCamelCase__ ,  clean_up_tokenization_spaces=UpperCamelCase__   )
    UpperCAmelCase__         = batch["""ids"""]
    if num_return_sequences > 1:
      UpperCAmelCase__         = chunks(UpperCamelCase__ ,  UpperCamelCase__   )  # batch size chunks, each of size num_return_seq
    for i, pred in enumerate(UpperCamelCase__   ):
      results.append({"""pred""": pred, """id""": ids[i].item()}   )
  save_json(UpperCamelCase__ ,  UpperCamelCase__   )
  return results, sampler.num_replicas


def     lowercase		(  ):
  UpperCAmelCase__         = argparse.ArgumentParser(
      epilog="""Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate"""   )
  parser.add_argument("""--data_dir""" ,  type=UpperCamelCase__ ,  help="""like cnn_dm/test.source"""   )
  parser.add_argument(
      """--model_name""" ,  type=UpperCamelCase__ ,  help="""like facebook/bart-large-cnn,t5-base, etc.""" ,  default="""sshleifer/distilbart-xsum-12-3""" ,  )
  parser.add_argument("""--save_dir""" ,  type=UpperCamelCase__ ,  help="""where to save""" ,  default="""tmp_gen"""   )
  parser.add_argument("""--max_source_length""" ,  type=UpperCamelCase__ ,  default=UpperCamelCase__   )
  parser.add_argument(
      """--type_path""" ,  type=UpperCamelCase__ ,  default="""test""" ,  help="""which subset to evaluate typically train/val/test"""   )
  parser.add_argument("""--task""" ,  type=UpperCamelCase__ ,  default="""summarization""" ,  help="""used for task_specific_params + metrics"""   )
  parser.add_argument("""--bs""" ,  type=UpperCamelCase__ ,  default=8 ,  required=UpperCamelCase__ ,  help="""batch size"""   )
  parser.add_argument(
      """--local_rank""" ,  type=UpperCamelCase__ ,  default=-1 ,  required=UpperCamelCase__ ,  help="""should be passed by distributed.launch"""   )

  parser.add_argument(
      """--n_obs""" ,  type=UpperCamelCase__ ,  default=UpperCamelCase__ ,  required=UpperCamelCase__ ,  help="""How many observations. Defaults to all."""   )
  parser.add_argument(
      """--num_return_sequences""" ,  type=UpperCamelCase__ ,  default=1 ,  required=UpperCamelCase__ ,  help="""How many sequences to return"""   )
  parser.add_argument(
      """--sync_timeout""" ,  type=UpperCamelCase__ ,  default=600 ,  required=UpperCamelCase__ ,  help="""How long should master process wait for other processes to finish.""" ,  )
  parser.add_argument("""--src_lang""" ,  type=UpperCamelCase__ ,  default=UpperCamelCase__ ,  required=UpperCamelCase__   )
  parser.add_argument("""--tgt_lang""" ,  type=UpperCamelCase__ ,  default=UpperCamelCase__ ,  required=UpperCamelCase__   )
  parser.add_argument(
      """--prefix""" ,  type=UpperCamelCase__ ,  required=UpperCamelCase__ ,  default=UpperCamelCase__ ,  help="""will be added to the begininng of src examples"""   )
  parser.add_argument("""--fp16""" ,  action="""store_true"""   )
  parser.add_argument("""--debug""" ,  action="""store_true"""   )
  UpperCAmelCase__         = time.time()
  UpperCAmelCase__    ,       UpperCAmelCase__         = parser.parse_known_args()
  UpperCAmelCase__         = parse_numeric_n_bool_cl_kwargs(UpperCamelCase__   )
  if generate_kwargs and args.local_rank <= 0:
    print(F'''parsed the following generate kwargs: {generate_kwargs}'''   )
  UpperCAmelCase__         = Path(args.save_dir + """_tmp"""   )
  Path(UpperCamelCase__   ).mkdir(exist_ok=UpperCamelCase__   )  # this handles locking.
  UpperCAmelCase__         = list(json_save_dir.glob("""rank_*.json"""   )   )
  if intermediate_files:
    raise ValueError(F'''Found files at {json_save_dir} please move or remove them.'''   )
    # In theory, a node could finish and save before another node hits this. If this happens, we can address later.
  UpperCAmelCase__         = {}
  if args.src_lang is not None:
    UpperCAmelCase__         = args.src_lang
  if args.tgt_lang is not None:
    UpperCAmelCase__         = args.tgt_lang

  Path(args.save_dir   ).mkdir(exist_ok=UpperCamelCase__   )
  UpperCAmelCase__    ,       UpperCAmelCase__         = eval_data_dir(
      args.data_dir ,  UpperCamelCase__ ,  args.model_name ,  type_path=args.type_path ,  bs=args.bs ,  fpaa=args.fpaa ,  task=args.task ,  local_rank=args.local_rank ,  n_obs=args.n_obs ,  max_source_length=args.max_source_length ,  num_return_sequences=args.num_return_sequences ,  prefix=args.prefix ,  dataset_kwargs=UpperCamelCase__ ,  **UpperCamelCase__ ,  )

  if args.local_rank <= 0:
    UpperCAmelCase__         = Path(args.save_dir   )
    save_dir.mkdir(exist_ok=UpperCamelCase__   )
    UpperCAmelCase__         = gather_results_from_each_node(UpperCamelCase__ ,  UpperCamelCase__ ,  args.sync_timeout   )
    UpperCAmelCase__         = combine_partial_results(UpperCamelCase__   )
    if args.num_return_sequences > 1:
      UpperCAmelCase__         = save_dir.joinpath("""pseudolabel_results.json"""   )
      print(F'''Saving aggregated results at {save_path}, intermediate in {json_save_dir}/'''   )
      save_json(UpperCamelCase__ ,  UpperCamelCase__   )
      return
    UpperCAmelCase__         = Path(args.data_dir   ).joinpath(args.type_path + """.target"""   )
    with open(UpperCamelCase__   ) as f:
      UpperCAmelCase__         = [x.rstrip() for x in f.readlines()][: len(UpperCamelCase__   )]

    # Calculate metrics, save metrics,  and save _generations.txt
    UpperCAmelCase__         = """translation""" in args.task
    UpperCAmelCase__         = calculate_bleu if calc_bleu else calculate_rouge
    UpperCAmelCase__         = """bleu""" if calc_bleu else """rouge"""
    UpperCAmelCase__         = score_fn(UpperCamelCase__ ,  UpperCamelCase__   )
    UpperCAmelCase__         = len(UpperCamelCase__   )
    UpperCAmelCase__         = time.time() - start_time
    UpperCAmelCase__         = round(runtime / metrics["""n_obs"""] ,  4   )
    UpperCAmelCase__         = num_replicas
    # TODO(@stas00): add whatever metadata to metrics
    UpperCAmelCase__         = save_dir.joinpath(F'''{args.type_path}_{metric_name}.json'''   )
    save_json(UpperCamelCase__ ,  UpperCamelCase__ ,  indent=UpperCamelCase__   )
    print(UpperCamelCase__   )
    write_txt_file(UpperCamelCase__ ,  save_dir.joinpath(F'''{args.type_path}_generations.txt'''   )   )
    if args.debug:
      write_txt_file(UpperCamelCase__ ,  save_dir.joinpath(F'''{args.type_path}.target'''   )   )
    else:
      shutil.rmtree(UpperCamelCase__   )


def     lowercase		(  _lowerCAmelCase   ):
  UpperCAmelCase__         = []
  for partial_result in partial_results:
    records.extend(UpperCamelCase__   )
  UpperCAmelCase__         = sorted(UpperCamelCase__ ,  key=lambda _lowerCAmelCase   : x["id"]   )
  UpperCAmelCase__         = [x["""pred"""] for x in records]
  return preds


def     lowercase		(  _lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase   ):
  # WAIT FOR lots of .json files
  UpperCAmelCase__         = time.time()
  logger.info("""waiting for all nodes to finish"""   )
  UpperCAmelCase__         = None
  while (time.time() - start_wait) < timeout:
    UpperCAmelCase__         = list(save_dir.glob("""rank_*.json"""   )   )
    if len(UpperCamelCase__   ) < num_replicas:
      continue
    try:
      # make sure all json files are fully saved
      UpperCAmelCase__         = lmap(UpperCamelCase__ ,  UpperCamelCase__   )
      return json_data
    except JSONDecodeError:
      continue
  else:
    raise TimeoutError("""Rank 0 gave up on waiting for other processes"""   )
  # Unreachable


if __name__ == "__main__":
   # Usage for MT:
   run_generate()





'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase					={
    "configuration_mobilebert": [
        "MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileBertConfig",
        "MobileBertOnnxConfig",
    ],
    "tokenization_mobilebert": ["MobileBertTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase					=["MobileBertTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase					=[
      "MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "MobileBertForMaskedLM",
      "MobileBertForMultipleChoice",
      "MobileBertForNextSentencePrediction",
      "MobileBertForPreTraining",
      "MobileBertForQuestionAnswering",
      "MobileBertForSequenceClassification",
      "MobileBertForTokenClassification",
      "MobileBertLayer",
      "MobileBertModel",
      "MobileBertPreTrainedModel",
      "load_tf_weights_in_mobilebert",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase					=[
      "TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFMobileBertForMaskedLM",
      "TFMobileBertForMultipleChoice",
      "TFMobileBertForNextSentencePrediction",
      "TFMobileBertForPreTraining",
      "TFMobileBertForQuestionAnswering",
      "TFMobileBertForSequenceClassification",
      "TFMobileBertForTokenClassification",
      "TFMobileBertMainLayer",
      "TFMobileBertModel",
      "TFMobileBertPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_mobilebert import (
      MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      MobileBertConfig,
      MobileBertOnnxConfig,
  )
  from .tokenization_mobilebert import MobileBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mobilebert_fast import MobileBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mobilebert import (
        MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        MobileBertForMaskedLM,
        MobileBertForMultipleChoice,
        MobileBertForNextSentencePrediction,
        MobileBertForPreTraining,
        MobileBertForQuestionAnswering,
        MobileBertForSequenceClassification,
        MobileBertForTokenClassification,
        MobileBertLayer,
        MobileBertModel,
        MobileBertPreTrainedModel,
        load_tf_weights_in_mobilebert,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_mobilebert import (
        TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFMobileBertForMaskedLM,
        TFMobileBertForMultipleChoice,
        TFMobileBertForNextSentencePrediction,
        TFMobileBertForPreTraining,
        TFMobileBertForQuestionAnswering,
        TFMobileBertForSequenceClassification,
        TFMobileBertForTokenClassification,
        TFMobileBertMainLayer,
        TFMobileBertModel,
        TFMobileBertPreTrainedModel,
    )

else:
  import sys

  __UpperCAmelCase					=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
